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<title>org.terrier.matching.models package</title>
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<p>Provides the classes that implement various weighting models. Generally, the models fall into two classes: </p>
<ul>
<li>Term weighting models - score an occurrence of a document regardless of where the occurrences appear.</li>
<li>Field-based weighting models - score an occurrence of a document depending on the fields that the term occurs in.</li>
</ul>
Various different families of weighting models are implemented:
<ul>
<li>IDF: e.g. <a href="TF_IDF.html">TF_IDF</a> (aka BM11), <a href="BM25.html">BM25</a>, <a href="LemurTF_IDF.html">LemurTF_IDF</a>.</li>
<li>Divergence from Randomness: e.g. <a href="PL2.html">PL2</a>, <a href="InL2.html">InL2</a>. Arbitrary DFR models can also be generated
using <a href="DFRWeightingModel.html">DFRWeightingModel</a></li>
<li>hyper-geometric Divergence from Randomness: e.g. <a href="DLH.html">DLH</a>, <a href="DPH.html">DPH</a>, <a href="DFRee.html">DFRee</a></li>
<li>Language modelling: e.g. <a href="Hiemstra_LM.html">Hiemstra_LM</a>, <a href="Dirichlet_LM.html">Dirichlet_LM</a></li>
<li>Divergence from Independence: <a href="DFI0.html">DFI0</a></li>
</ul>

Of the field-based models, the following are implemented:
<ul>
<li><a href="BM25F.html">BM25F</a></li>

<li><a href="PL2F.html">PL2F</a></li>
<li><a href="MDL2.html">MDL2</a></li>
<li><a href="ML2.html">ML2</a></li>
<li>Arbitrary per-field normalisation models can be generated using the <a href="PerFieldNormWeightingModel.html">PerFieldNormWeightingModel</a> model.</li>
</ul>
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